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{
"cells": [
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"# !pip install datasets\n",
"\n",
"from datasets import load_dataset"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"home_values_forecasts\n",
"new_constructions\n",
"for_sale_listings\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"Downloading data: 100%|██████████| 215M/215M [00:05<00:00, 37.3MB/s] \n",
"Generating train split: 693661 examples [00:20, 34052.02 examples/s]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"rentals\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"Downloading data: 100%|██████████| 413M/413M [00:12<00:00, 34.2MB/s] \n",
"Generating train split: 1258740 examples [00:28, 44715.39 examples/s]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"sales\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"Downloading data: 100%|██████████| 280M/280M [00:06<00:00, 41.1MB/s] \n",
"Generating train split: 504608 examples [00:19, 25569.29 examples/s]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"home_values\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"Downloading data: 100%|██████████| 47.3M/47.3M [00:01<00:00, 29.7MB/s]\n",
"Generating train split: 117912 examples [00:03, 35540.83 examples/s]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"days_on_market\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"Generating train split: 586714 examples [00:16, 34768.33 examples/s]\n"
]
}
],
"source": [
"configs = [\n",
" \"home_values_forecasts\",\n",
" \"new_construction\",\n",
" \"for_sale_listings\",\n",
" \"rentals\",\n",
" \"sales\",\n",
" \"home_values\",\n",
" \"days_on_market\",\n",
"]\n",
"for config in configs:\n",
" print(config)\n",
" dataset = load_dataset(\"misikoff/zillow\", config, trust_remote_code=True)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'Region ID': '102001',\n",
" 'Size Rank': 0,\n",
" 'Region': 'United States',\n",
" 'Region Type': 'country',\n",
" 'State': None,\n",
" 'Home Type': 'SFR',\n",
" 'Date': '2015-01-31',\n",
" 'Rent (Smoothed)': 1251.1195068359375,\n",
" 'Rent (Smoothed) (Seasonally Adjusted)': 1253.3807373046875}"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"next(iter((dataset[\"train\"])))"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"gen = iter((dataset[\"train\"]))"
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'Region ID': '102001',\n",
" 'Size Rank': 0,\n",
" 'Region': 'United States',\n",
" 'Region Type': 'country',\n",
" 'State': None,\n",
" 'Home Type': 'condo/co-op only',\n",
" 'Date': '2018-03-31',\n",
" 'Sale Price': 386700.0,\n",
" 'Sale Price per Sqft': 238.31776428222656,\n",
" 'Count': 4267}"
]
},
"execution_count": 37,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"next(gen)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "sta663",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.12.2"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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